Learn to design, train, optimize, integrate and deploy genai models like chatgpt, GANs (Generative Adversarial Networks)
β±οΈ Length: 9.2 total hours
β 4.26/5 rating
π₯ 17,053 students
π September 2025 update
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- Course Overview
- Master the core principles and advanced applications of Generative AI, including models like ChatGPT and GANs.
- Engage in practical, hands-on training to design, train, optimize, integrate, and deploy generative models.
- Benefit from a curriculum meticulously updated to September 2025, ensuring current techniques and industry best practices.
- Understand the complete lifecycle of generative models, from foundational theory to production deployment and monitoring.
- Explore diverse, cutting-edge generative architectures: Diffusion Models, Variational Autoencoders (VAEs), and advanced Transformers.
- Designed for aspiring AI engineers, researchers, and data scientists aiming for mastery in creating AI-generated content.
- Learn responsible AI development, incorporating critical ethical considerations specific to generative systems.
- Requirements / Prerequisites
- A strong foundation in Python programming, including essential libraries like NumPy and Pandas.
- Basic understanding of Machine Learning and Deep Learning principles and neural networks.
- Familiarity with fundamental mathematical concepts: linear algebra, calculus, and statistics.
- Access to a computing environment with GPU capabilities (e.g., cloud instances) for efficient model training is recommended.
- Prior exposure to deep learning frameworks (PyTorch/TensorFlow) is advantageous, though not strictly required.
- A keen interest in innovation and exploring the creative, transformative applications of Artificial Intelligence.
- Skills Covered / Tools Used
- Proficiency in advanced Generative Model Architectures: Transformers, GANs, Diffusion, and VAEs.
- Expertise in Model Design and Customization for innovative generative solutions.
- Techniques for Efficient Model Training and Optimization, including hyperparameter tuning.
- Specialized methods for Generative Data Preprocessing and Augmentation for improved model performance.
- Skills in advanced Model Evaluation and Quality Assurance for generative outputs.
- Mastery of Prompt Engineering and Fine-tuning large foundation models for specific needs.
- Practical application of robust Deployment Strategies and MLOps for Generative AI models.
- Implementation of Ethical AI Development and Bias Mitigation strategies.
- Hands-on experience with industry-standard Deep Learning Frameworks (PyTorch, TensorFlow).
- Utilizing major Cloud AI Platforms (AWS, Google Cloud, Azure) for scalable workloads.
- Effective use of Version Control (Git) for collaborative Generative AI projects.
- Integration of generative models via APIs and microservices into applications.
- Monitoring and maintenance techniques for deployed generative AI systems.
- Strategies to address common generative model challenges, like mode collapse.
- Understanding of adversarial robustness and security in generative AI.
- Techniques for generating diverse data types: text, images, code, and synthetic datasets.
- Benefits / Outcomes
- Achieve comprehensive proficiency in the end-to-end Generative AI lifecycle.
- Qualify for high-demand roles: Generative AI Engineer, ML Engineer, AI Researcher.
- Build a robust portfolio of practical Generative AI projects showcasing capabilities.
- Make informed technical and strategic decisions for businesses leveraging generative AI.
- Develop the ability to innovate and create novel AI applications across sectors.
- Establish a strong foundation for advanced research or further specialized AI studies.
- Gain confidence in solving complex, real-world Generative AI implementation challenges.
- Stay current with the rapidly evolving AI landscape and emerging generative techniques.
- Contribute significantly to the responsible, ethical, and impactful development of AI.
- PROS
- Highly Practical and Project-Oriented: Strong emphasis on hands-on application and deployable skills.
- Up-to-Date Curriculum: Content updated to September 2025 ensures current industry standards.
- Strong Industry Relevance: Directly prepares learners for cutting-edge Generative AI roles.
- Comprehensive Skill Set Development: Covers the entire lifecycle from design to deployment.
- High Student Satisfaction: Evidenced by a 4.26/5 rating and 17,053 students.
- CONS
- The condensed 9.2-hour format, despite its “Master” title, implies those seeking exhaustive theoretical academic depth will require supplementary learning.
Learning Tracks: English,IT & Software,Other IT & Software
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